---
library_name: sklearn
tags:
- sklearn
- skops
- tabular-regression
widget:
structuredData:
AveBedrms:
- 0.9806451612903225
- 1.0379746835443038
- 0.9601449275362319
AveOccup:
- 2.587096774193548
- 2.8658227848101268
- 2.6449275362318843
AveRooms:
- 7.275268817204301
- 5.39493670886076
- 6.536231884057971
HouseAge:
- 38.0
- 25.0
- 39.0
Latitude:
- 37.44
- 37.31
- 34.16
Longitude:
- -122.19
- -122.03
- -118.07
MedInc:
- 9.3198
- 5.3508
- 6.4761
Population:
- 1203.0
- 1132.0
- 730.0
---
# Model description
[More Information Needed]
## Intended uses & limitations
[More Information Needed]
## Training Procedure
### Hyperparameters
The model is trained with below hyperparameters.
Click to expand
| Hyperparameter | Value |
|--------------------------|---------------|
| bootstrap | True |
| ccp_alpha | 0.0 |
| criterion | squared_error |
| max_depth | |
| max_features | 1.0 |
| max_leaf_nodes | |
| max_samples | |
| min_impurity_decrease | 0.0 |
| min_samples_leaf | 1 |
| min_samples_split | 2 |
| min_weight_fraction_leaf | 0.0 |
| n_estimators | 100 |
| n_jobs | |
| oob_score | False |
| random_state | |
| verbose | 0 |
| warm_start | False |
RandomForestRegressor()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
RandomForestRegressor()